Short-Term Streamflow Forecasting Using Hybrid Deep Learning Model Based on Grey Wolf Algorithm for Hydrological Time Series

Kilinç, Hüseyin Çağan; Yurtsever, Adem

doi:10.3390/su14063352

Cited by 33 publications

(16 citation statements)

References 59 publications

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“…The authors showed the advantages of the hybridization based on DL algorithms, achieving accurate predictions with R 2 values up to 0.98. The results obtained by Kilinc and Yurtsever 40 are in line with the prediction obtained for several rivers investigated in the present study for t = 1 day. However, they did not perform an analysis with increasing time horizon, as made in the present study.…”

Section: Discussionsupporting

confidence: 92%

“…The authors calculated values of NSE up to 0.48, also showing a performance reduction as the forecast horizon increased, as observed in the present study. Kilinc and Yurtsever 40 also developed a hybrid DL model Based on Grey Wolf algorithm (GWO) and GRU for the daily streamflow forecasting in two stations located in the Seyhan basin, Turkey. The authors showed the advantages of the hybridization based on DL algorithms, achieving accurate predictions with R 2 values up to 0.98.…”

Section: Discussionmentioning

confidence: 99%

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Short-term forecasts of streamflow in the UK based on a novel hybrid artificial intelligence algorithm

Nunno

Marinis

Granata

2023

Sci Rep

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In recent years, the growing impact of climate change on surface water bodies has made the analysis and forecasting of streamflow rates essential for proper planning and management of water resources. This study proposes a novel ensemble (or hybrid) model, based on the combination of a Deep Learning algorithm, the Nonlinear AutoRegressive network with eXogenous inputs, and two Machine Learning algorithms, Multilayer Perceptron and Random Forest, for the short-term streamflow forecasting, considering precipitation as the only exogenous input and a forecast horizon up to 7 days. A large regional study was performed, considering 18 watercourses throughout the United Kingdom, characterized by different catchment areas and flow regimes. In particular, the predictions obtained with the ensemble Machine Learning-Deep Learning model were compared with the ones achieved with simpler models based on an ensemble of both Machine Learning algorithms and on the only Deep Learning algorithm. The hybrid Machine Learning-Deep Learning model outperformed the simpler models, with values of R2 above 0.9 for several watercourses, with the greatest discrepancies for small basins, where high and non-uniform rainfall throughout the year makes the streamflow rate forecasting a challenging task. Furthermore, the hybrid Machine Learning-Deep Learning model has been shown to be less affected by reductions in performance as the forecasting horizon increases compared to the simpler models, leading to reliable predictions even for 7-day forecasts.

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Section: Discussionsupporting

confidence: 92%

Section: Discussionmentioning

confidence: 99%

Short-term forecasts of streamflow in the UK based on a novel hybrid artificial intelligence algorithm

Nunno

Marinis

Granata

2023

Sci Rep

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“…The deep learning hybrid model, known as the gray wolf algorithm (GWO)-based recurrent gated unit (GRU) (GWO-GRU), was developed by 42 for forecasting daily flow rates, utilizing its antecedents as input variables. The proposed model was compared with a linear model.…”

Section: Introductionmentioning

confidence: 99%

Short-term streamflow modeling using data-intelligence evolutionary machine learning models

Martinho,

Hippert,

Goliatt

2023

Sci Rep

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Accurate streamflow prediction is essential for efficient water resources management. Machine learning (ML) models are the tools to meet this need. This paper presents a comparative research study focusing on hybridizing ML models with bioinspired optimization algorithms (BOA) for short-term multistep streamflow forecasting. Specifically, we focus on applying XGB, MARS, ELM, EN, and SVR models and various BOA, including PSO, GA, and DE, for selecting model parameters. The performances of the resulting hybrid models are compared using performance statistics, graphical analysis, and hypothesis testing. The results show that the hybridization of BOA with ML models demonstrates significant potential as a data-driven approach for short-term multistep streamflow forecasting. The PSO algorithm proved superior to the DE and GA algorithms in determining the optimal hyperparameters of ML models for each step of the considered time horizon. When applied with all BOA, the XGB model outperformed the others (SVR, MARS, ELM, and EN), best predicting the different steps ahead. XGB integrated with PSO emerged as the superior model, according to the considered performance measures and the results of the statistical tests. The proposed XGB hybrid model is a superior alternative to the current daily flow forecast, crucial for water resources planning and management.

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“…Streamflow forecasting plays an important role in water resources planning and management in both the short and long term [1,2]. Accordingly, developing a precise and reliable model for streamflow forecasting is of high significance [3].…”

Section: Introductionmentioning

confidence: 99%

Improving Daily Streamflow Forecasting Using Deep Belief Net-Work Based on Flow Regime Recognition

Shen

Zou

Dong

et al. 2022

Water

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Streamflow forecasting is of great significance for water resources planning and management. In recent years, numerous data-driven models have been widely used for streamflow forecasting. However, the traditional single data-driven model ignores the utilization of different streamflow regimes. This study proposed an integrated framework for daily streamflow forecasting based on the regime recognition of flow sequences. The framework integrates self-organizing maps (SOM) for identifying streamflow sub-sequences, the random forests (RF) algorithm to select input variables for different streamflow sub-sequences, and a deep belief network (DBN) for establishing complex relationships between the selected input variables and streamflows for different sub-sequences. Specifically, the integrated framework was applied to forecast daily streamflow at the Xiantao hydrological station in the Hanjiang River Basin, China. The results show that the developed integrated framework has higher streamflow prediction accuracy than the single data-driven model (i.e., the DBN model in this study), with Nash efficiency coefficient (NSE) of 0.91/0.81 and coefficient of determination (R2) of 0.93/0.89 for the integrated framework/DBN model during the validation period, respectively. Additionally, the prediction accuracy of the peak flood was also improved. The relative error of the peak flood derived from the integrated framework was reduced by 4.6%, compared with the single DBN model. Overall, the constructed integration framework, considering the complex characteristic of different flow regimes, could improve the accuracy for daily streamflow forecasting.

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Short-Term Streamflow Forecasting Using Hybrid Deep Learning Model Based on Grey Wolf Algorithm for Hydrological Time Series

Cited by 33 publications

References 59 publications

Short-term forecasts of streamflow in the UK based on a novel hybrid artificial intelligence algorithm

Short-term forecasts of streamflow in the UK based on a novel hybrid artificial intelligence algorithm

Short-term streamflow modeling using data-intelligence evolutionary machine learning models

Improving Daily Streamflow Forecasting Using Deep Belief Net-Work Based on Flow Regime Recognition

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